Optimization Study of Biomass Hydrogenation to Ethylene Glycol Using Response Surface Methodology

Abstract

Statistical-based study using response surface methodology (RSM) was conducted to study the effects of process parameters towards biomass hydrogenation. Using Malaysian oil palm empty fruit bunches (EFB) fibres as feedstock, the central composite design (CCD) technique was employed and 18 runs were generated by CCD when four parameters (mass ratio of binary catalyst, hydrogen pressure, temperature and mass ratio of catalyst to feedstock) were varied with two center points to determine the effects of process parameters and eventually to get optimum ethylene glycol (EG) yield. RSM with quadratic function was generated for biomass hydrogenation, indicating all factors except temperature, were important in determining EG yield. Analysis of variance (ANOVA) showed a high coefficient of determination (R2) value of >0.98, ensuring a satisfactory prediction of the quadratic model with experimental data. The quadratic model suggested the optimum EG yield should be >25 wt.% and the EG yield results were successfully reproduced in the laboratory.

Statistical Optimization of Alkali Pretreatment to Improve Sugars Recovery from Spent Coffee Grounds and Utilization in Lactic Acid Fermentation

“…Tables 3-5 show the ANOVA results for the response surface quadratic model. A high F-value and low p-value (p < 0.05) indicate that the model and the model terms are significant [31]. The F-values of each model (GC, MC and ED) were found to be 5.21, 5.45 and 16.87, respectively.…”

Production of Monoethylene Glycol from Lignocellulosic Biomass via Catalytic Hydrogenation: A Review

“…Common pre-treatment method chosen are alkaline and mechanical pre-treatment. Most researchers found that cellulose content and MEG yield increase up to 25% when using alkaline treatment such as sodium hydroxide, butanediol, and hydrogen peroxide [5], [9], [10]. Pan et al (2016) state that the treated lignocellulose appears rougher and more porous physically than without treatment when observe using SEM machines, making it easier to convert the cellulose to MEG.…”

“…Both temperature and hydrogen pressure have a different role in catalytic hydrogenation process, i.e. hydrogen gas pressure affects the hydrogenation reaction while temperature affects the retro-aldol reactivity [5]. A study on catalytic hydrogenation of papaya waste found that the yield of MEG and 1,2propylene glycol increased when the temperature and pressure of hydrogen gas increased to 245 ºC and 6 MPa respectively [14].…”

“…Hence, catalytic hydrogenation process which use lignocellulosic biomass is attracting the researchers' attention to substitute the conventional and fermentation methods. However, this process requires high temperature and pressure as well as the presence of catalysts to overcome the activation energy [5], [6]. Thus, this review will discuss on the production of MEG via…”

Optimization of Ethylene Glycol Production Process from Bio Mass (Corn Cob) Using Response Surface Methodology (RSM)

“…Extensive work has been carried out by researchers around the world who have studied the effect of process parameters to monoethylene glycol (MEG) yield". Law et al [11] carried out "optimization study of biomass hydrogenation to ethylene glycol using response surface methodology. The following results were gotten, Using Malaysian oil palm empty fruit bunches (EFB) fibres as feedstock, the central composite design (CCD) technique was employed and 18 runs were generated by CCD when four parameters (mass ratio of binary catalyst, hydrogen pressure, temperature and mass ratio of catalyst to feedstock) were varied with two center points to determine the effects of process parameters and eventually to get optimum ethylene glycol (EG) yield".…”